Practical Approaches to Tumor Xenograft Analysis. Frank Voelker, Flagship Biosciences LLC Trevor Johnson, Flagship Biosciences LLC Veronica Traviglone, Infinity Pharmaceuticals Igor Deyneko, Infinity Pharmaceuticals. Outline. Introduction Anatomy of a Xenograft
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Anatomy of a Xenograft
Defining the Approach to the Analysis
Examples of Cases
Presenting FACTS for Target Tissue Analysis
CD-31 Stain for Microvessels
Adjacent tissue frequently contains host responses such as inflammation, adipose tissue or adnexal structures that need to be excluded from the analysis
Necrosis and neoplastic cells in various stages of degeneration are common features of a xenograft.
Even the smallest microvessels may be enclosed in adventitial tissue as an extension of stromal ingrowths
Compare microscopic characteristics of an H&E- stained section with comparable regions of an IHC CD31-stained section
This will provide valuable perspective regarding IHC target tissue staining and will allow more accurate identification of tissue classes.
Which of these do you want to include in your analysis?
Neoplastic Cell Area
Total Xenograft Area
A function of neoplastic cell abundance versus necrosis and/or stromal prominence
Marker Area or Score
Neoplastic Cell Area
Immunohistochemistry expression of marker metabolism within neoplastic cell population
Number of Neoplastic Nuclei
Neoplastic Cell Area
An indication of mean neoplastic cell size
Targeted Cell Number
Total Xenograft Area
Cell frequency within measured area
Number of Microvessels
Neoplastic Cell + Stromal Area
Staining variation in tumors or other tissues frequently raises the question of whether to measure either percent area or average intensity of an immunostain.
In some cases, a solution to this problem is to measure score as an output convention encompassing both percent area and stain intensity.
(Nwp/Ntotal)x(100) + Np/Ntotal)x(200) + Nsp/Ntotal)x(300) = “H Score”
(For a maximum of 300)
Similar IHC staining of fibronectin and secretion droplets in this xenograft tumor with subsequent poor differentiation by the Genie™ classifier required the use of the negative pen tool to assist in quantitating fibronectin using the IHC deconvolution algorithm.
Use of a 21UX Cintiq Wacom drawing board facilitates the use of positive and negative pen tools in manually delineating critical features of the xenograft.
Using Genie™ to Segregate Neoplasm from Nontarget Tissue in a Xenograft Stained for Phospho-Histone 3
Central regions of the xenograft contain an interdigitating pattern of necrosis and connective tissue trabeculae too complex for manual exclusion .
Accurate segregation of neoplasm (green) from necrosis and connective tissue (red) was accomplished using Genie™
Using software to preprocess an image with the purpose of segregating target tissue components from nontarget tissue.
Subsequent analysis yields accurate data only from the target tissue component, and omits erroneous nonspecific results from nontarget tissue.
Feature recognition is valuable in xenograft analysis when target and nontarget regions are too intricately interwoven for manual exclusion.
Xenograft neoplasms within a study are surprisingly heterogeneous even though derived from the same source, and it is difficult and time consuming to derive a common pattern recognition classifier appropriate for all neoplasms within a study. It is more expeditious and also yields more accuracy to develop new classifiers as the analysis progresses.
Progression of analysis through sample series
Red = 1st Classifier
Blue = 2nd Classifier
Green = 3rd Classifier
* = Points when a new classifier was developed during the analysis
Even though multiple classifiers are constructed for successive samples, the final analyses of target tissue components will be accurate and comparable if the same algorithm threshold values are constantly maintained.
Subsequent Uniform Analysis of Segregated Target Tissue for area/intensity
Higher magnification reveals accurate separation of target from nontarget tissue.
The grayed-out nontarget tissue class consists of a mixture of connective tissue trabeculae and necrotic tumor cells.
Original IHC Image
Assessing the intensity and quantity of fibronectin as a marker in xenograft stroma
Aperio Deconvolution Mark-up
Original CD31-Stained Image
Use of the microvessel analysis algorithm to assess angiogenesis in a xenograft neoplasm in a mouse
Aperio Microvessel Algorithm Mark-up
Microvessel analysis provides important information regarding potential antineoplastic effects of pharmaceutical compounds
Use of the microvessel analysis algorithm to assess macrophage populations in mouse xenograft neoplasms
Threshold algorithm parameters are modified to accommodate the smaller size and shape characteristics of the cells
Discriminating between nuclear and cytoplasmic regions of a neoplasm allows separate biomarker intensity measurement for both nuclear and cytoplasmic markers (analysis with Visiopharm software).
Distinctive or Special Staining Facilitates Target Tissue Pattern Recognition
First pass accuracy is much greater for the H&E-stained section than for the hematoxylin- counterstained IHC section
Current histology pattern recognition programs often have difficulty distinguishing target tissue profiles because of low contrast /nonspecific counterstaining
4. QC and
u(x,y) -> v(x,y)
Rotation: x’ = x•cos(θ) - y•sin(θ)
y’ = x•sin(θ) + y•cos(θ)
Translation: x -> x’
y -> y’
℮xp(2 ∏ i (u x’ + v y’)) = ƒ(f) ƒ(g) / | ƒ(f) ƒ(g) |
MI(X,Y) = H(Y) – H(Y | X) = H(X) – H(Y) – H(X,Y)
where H(X) = Ex(log(P(X))
Decompose image into collection of appropriate wavelet (Spline, Haar, Etc.)
Filter image along rows and columns (high and low pass)
Find frequency coefficients and apply differential measurements
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